Jurnal Ilmiah Komputer dan Informatika KOMPUTA
1
Edisi 1 Volume 1, Februari 2016 ISSN : 2089-9033
DEVELOPMENT OF DATA WAREHOUSE ON PT.PUPUK ISKANDAR MUDA
Dinda Wilanda Program Studi Teknik Informatika
Fakultas Teknik dan Ilmu Komputer Universitas Komputer Indonesia Jl. Dipati Ukur. 112-116 Bandung
E-mail : Dinwil07gmail.com
ABSTRAK
PT.Pupuk Iskandar Muda or commonly called as PT PIM is a subsidiary company of
PT.Pupuk Indonesia Persero STATE-OWNED ENTERPRISES that working on chemical industry
in particular produce urea fertilizer. The exixtence of PT.Pupuk Iskandar Muda can supply the needs of
fertilizer to farmers and the vast plantations in some parts of the province of Aceh, North Sumatra, West
Sumatra, Riau, Riau Islands, and West Kalimantan. Current amount of existing data on the PT. Pupuk
Iskandar Muda is enormous , it makes delay in accessing the information, and every region has a
different data type and naming. The form of the report is not yet support for the views of the various
aspects of the companys decision-making, so it needed a system that can display information in a
multi dimensiona. Therefore, the company needs data warehouse for managing production and sales
data for accessing information to making a decision.
The company’s data used for data warehouse including sales data, production data, stockroom,
customer, product, factory, ingredients. Data warehouse makes the spread data on company to the
integrated data, so that can helping company for analyze existing data for making decision that
strategic to more quickly and accurately. The development of datawarehouse used modeling of
Fact constellations and OLAP On-Line Analytical Processing design, for the processing and analysis
of the data by using the technique of Roll-ups and Drill-Down.
The sofwatre of data warehouse can helping the company on integrate data. The result of
information can be used for helping the analysis for company of PT.Pupuk Iskandar Muda.
Key Word : Data warehouse, Fact Constellations,
kRoll-Up, Drill-Down, OLAP
1. INTRODUCTION
Data warehouse is a database that react with each other which can be used for query and
analysis, is the orientation of the subject, integrated, time-variant, and has not changed, which is used to
support decision making. Data warehouse is a system of extracting, cleaning, adjusting, and sends
the data source into the data storage and then support the implementation dimensional query and analysis
for decision-making purposes.
PT. Pupuk Iskandar Muda or commonly called the PT PIM is a subsidiary of PT. Pupuk
Indonesia Persero
under the
state-owned enterprises engaged in the chemical industry in
particular producing urea fertilizer. Hence the presence of PT. Pupuk Iskandar Muda fertilizer can
meet the needs of the vast estate in North Sumatra and several other regions provinces of Aceh, North
Sumatra, West Sumatra, Riau, Kepulauan Riau, and West Kalimantan.
Based on interviews with the PT. Pupuk Iskandar Muda, the current storage of large amounts
of data are still apart - separated. Companies that have large data sets to be analyzed for the continuity
of the companys business processes, in conducting an analysis on the information required data display
that can display a lot of data. Preparing reports on PT.Pupuk Iskndar Young conducted by collecting
data from every part of the company is needed, problems in making this report the need for a long
time. Form of the report does not yet support for the views of the various aspects of corporate decision-
making, so we need a system that can display information in a multi-dimensional and dynamic.
Therefore, the company requires a data warehouse to manage production data and sales data
to obtain information in making decisions.
1.1. Purpose and objectives
Based on the problems studied, the purpose of this research is to build a data warehouse software
in the PT. Pupuk Iskandar Muda as a solution to the slow and difficult analysis of the previous system.
While objectives to be achieved in the study are:
While the objectives to be achieved in the study are:
1. Assist the company in integrating data.
Jurnal Ilmiah Komputer dan Informatika KOMPUTA
2
Edisi 1 Volume 1, Februari 2016 ISSN : 2089-9033
2. Assisting the company in analyzing the sales
and production of goods in a given period is multidimensional.
2. LITERATURE
Data Warehouse can vary but have the same core, like the opinion of some experts the following:
Data Warehouse can vary but have the same core, like the opinion of some experts the
following: The data warehouse is a collection of data that have a nature-oriented subject, integrated, time-
variant, and is fixed on the collection of data in support of the decision making process management
[2]
. The data warehouse is a relational database
that is designed more to query and analysis of the transaction process, usually containing the data
history of the transaction process and could also data from other sources. Data warehouses separate
analysis workload from transaction workload and enables an organization to merge consolidation of
data from various sources [2].
The data warehouse is a method in the design of the database, which support the DSS Decission
Support System and EIS Executive Information System. Physically data warehouse is a database,
but the data warehouse and database design is very different. In traditional database design using
normalization, while the normalization of the data warehouse is not the best way [2].
From the definitions described above, it can be concluded that the data warehouse is a database
that react with each other can be used to query and analisisis, is the orientation of the subject,
integrated, time-variant, unchanged used to assist decision makers.
2.1 Basic Concepts Data Warehouse
The data warehouse is a collection of all sorts of data that is subject oriented, integrated, time
variant, and nonvolatile in support of the decision- making process [4].
Data warehouses are often integrated with various application systems to support the process of
reporting and data analysis by providing historical data, which provides the infrastructure for the EIS
and DSS.
a. Subject Oriented
The data warehouse is organized in major subjects, such as customers, items, and sales.
Focusing on the model and analysis on the data to make decisions, so its not on any
transaction or process is not in the OLTP. Avoid useless data in taking a decision.
b. Integrated
Built by connecting or uniting different data. relational databases, flat files, and on-line
transaction record. Ensuring consistency in the naming, coding structure, and structure
attributes of data between each other. c.
Datawarehouse time variant Data is stored to provide information from a
historical perspective, the data that year - last year or 4-5 years. Time is a key element of a
data warehouse at the time pengcaptures.
d. Non Volatile
Whenever the process of change, the data will be collected in each time. So it is not updated
continuously. Data warehouse does not require transaction processing and recovery. There are only
two operations initial loading of data and access of data.
2.2 ETL Process
Extraction, Transformation, Loading
The three main functions that need to be done to make the data ready for use in the data warehouse
is the extraction, transformation and loading. These three functions are in the staging area [5].
In this staging of data, provided the place and area with multiple functions such as data cleansing,
change, convert, and prepare the data to be stored and will be used in a data warehouse [5].
a. Extraction
Data Extraction is the process of taking the necessary data from the source data warehouse and
are then put on the staging area to be processed at a later stage. In this function are associated with
different types of data sources such as data formats, different machines, software and architecture are not
the same. So before the process is done, you should have to be defined requirement against data sources
that will be used for the next process.
b. Transformation
In fact, the process of transactional data is stored in various formats so rare to find a consistent
data between
existing applications.
Data transformation aimed at addressing this problem.
With this
data transformation
process, we
standardized the data on a consistent format. Some examples of such data inconsistencies can be caused
by different types of data, the data length and so forth.
c. Load
Data load is moving the data into the data warehouse. There are two loading data at the data
warehouse. The first is the initial load, this process is done when it has completed design and build a data
warehouse. The input data will be very large and takes a relatively longer. Second Incremental load,
carried out when the data warehouse is operated. Incremental load can be carried out in accordance
with a system built
